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Sci Data. 2014 Jun 10;1:140012. doi: 10.1038/sdata.2014.12. eCollection 2014.

Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control.

Author information

1
School of Biosciences, University of Birmingham , Edgbaston, Birmingham, B15 2TT, UK.
2
Department of Medicine, University of Alberta , Edmonton, AB, Canada T6G 2EI.
3
School of Biosciences, University of Birmingham , Edgbaston, Birmingham, B15 2TT, UK ; NERC Biomolecular Analysis Facility - Metabolomics Node (NBAF-B), University of Birmingham , Edgbaston, Birmingham, B15 2TT, UK.

Abstract

Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach for characterising molecular responses of organisms to disease, drugs and the environment. Increasingly large-scale metabolomics studies are being conducted, necessitating improvements in both bioanalytical and computational workflows to maintain data quality. This dataset represents a systematic evaluation of the reproducibility of a multi-batch DIMS metabolomics study of cardiac tissue extracts. It comprises of twenty biological samples (cow vs. sheep) that were analysed repeatedly, in 8 batches across 7 days, together with a concurrent set of quality control (QC) samples. Data are presented from each step of the workflow and are available in MetaboLights. The strength of the dataset is that intra- and inter-batch variation can be corrected using QC spectra and the quality of this correction assessed independently using the repeatedly-measured biological samples. Originally designed to test the efficacy of a batch-correction algorithm, it will enable others to evaluate novel data processing algorithms. Furthermore, this dataset serves as a benchmark for DIMS metabolomics, derived using best-practice workflows and rigorous quality assessment.

PMID:
25977770
PMCID:
PMC4381748
DOI:
10.1038/sdata.2014.12
[Indexed for MEDLINE]
Free PMC Article

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